Biology subjects

Stepanyants, A.

Publications and source records attributed to Stepanyants, A..

3 recordsLinked to original sources

bioRxiv2018

Artificial neural network filters for enhancing 3D optical microscopy images of neurites

The ability to extract accurate morphology of labeled neurons from microscopy images is crucial for mapping brain connectivity and for understanding changes in connectivity that underlie learning and memory formation. There are, however, two problems, specific to optical microscopy imaging of neurons, which make accurate neuron tracing exceedingly challenging: (i) neurites can appear broken due to inhomogeneous labeling and (ii) neurites can appear fused in 3D due to limited resolution. Here, we propose and evaluate several artificial neural network (NN) architectures and conventional image enhancement filters with the aim of solving both problems. To evaluate the effects of filtering, we examine the following four image quality metrics: normalized intensity in the cross-over regions between neurites, radius of neurites, coefficient of variation of intensity along neurites, and local background to neurite intensity ratio. Our results show that NN filters, trained on optimized semi-manual traces of neurites, can significantly outperform conventional filters. In particular, U-Net based filtering can virtually eliminate background intensity, while also reducing radius of neurites by 23% to nearly 1 voxel, decreasing intensity in the cross-over regions between neurites by 22%, and reducing variations in intensity along neurites by 26%. These results suggest that including a NN filtering step, which does not require much extra time or computing power, can be beneficial for neuron tracing projects.

neuroscience↗

bioRxiv2018

Accurate registration of 3D time-lapse microscopy images

In vivo imaging experiments often require automated detection and tracking of changes in the specimen. This problem, however, can be hindered by variations in the position and orientation of the specimen relative to the microscope, as well as by linear and nonlinear deformations. Here, we present a feature-based registration method, coupled with translation, rigid, affine, and B-spline transformations, designed to address these issues in 3D time-lapse microscopy images. In this method, features are detected as local intensity maxima in the source and target image stacks, and their similarity matrix is used as an input to the Hungarian algorithm to establish initial correspondences. Random Sampling Consensus algorithm is then employed to eliminate outliers. The resulting set of corresponding features is used to determine the optimal transformations. Accuracy of the proposed algorithm was tested on fluorescently labeled axons imaged over a 48-day period with a two-photon laser scanning microscope. For this, multiple axons in individual stacks of images were traced semi-manually in 3D, and the distances between the corresponding traces were measured before and after the registration. The results show that there is a progressive improvement in the registration accuracy with increasing complexity of the transformations. In particular, an accuracy of less than 1 voxel (0.26 m) was achieved with a regularized B-spline transformation. To illustrate the utility of the proposed method, registered images were used to automatically track synaptic boutons on axons over the entire duration of the experiment, yielding 99% precision and recall.

neuroscience↗

bioRxiv2018

Robust associative learning is sufficient to explain structural and dynamical properties of local cortical circuits

The ability of neural networks to associate successive states of network activity lies at the basis of many cognitive functions. Hence, we hypothesized that many ubiquitous structural and dynamical properties of local cortical networks result from associative learning. To test this hypothesis, we trained recurrent networks of excitatory and inhibitory neurons on memory sequences of varying lengths and compared network properties to those observed experimentally. We show that when the network is robustly loaded with near-maximum amount of associations it can support, it develops properties that are consistent with the observed probabilities of excitatory and inhibitory connections, shapes of connection weight distributions, overrepresentations of specific 3-neuron motifs, distributions of connection numbers in clusters of 3-8 neurons, sustained, irregular, and asynchronous firing activity, and balance of excitation and inhibition. What is more, memories loaded into the network can be retrieved even in the presence of noise comparable to the baseline variations in the postsynaptic potential. Confluence of these results suggests that many structural and dynamical properties of local cortical networks are simply a byproduct of associative learning.

neuroscience↗